Transcript Slide 1
TRB Planning Applications Conference May 5, 2013 Columbus, OH
SHRP2 C10A
Final Conclusions & Insights
Stephen Lawe, Joe Castiglione & John Gliebe
Resource Systems Group
C10A Project Objectives
Current models are limited
Not sufficiently sensitive to the dynamic interplay between travel behavior and network conditions Unable to represent the effects of policies such as variable road pricing and travel demand management strategies
Advanced model systems can better represent demand changes and network performance
Peak spreading, mode choices, destination choices Capacity and operational improvements such as signal coordination, freeway management and variable tolls, TDM
2
C10A Model System
Model components exchange information in a systematic and mutually dependent manner C10A model components Daysim “activity-based” model TRANSIMS network simulation model MOVES C10A linked model system implemented in both Jacksonville, FL and Burlington, VT “Linked” not “Integrated”
3
How are the model system components linked?
Daysim activity-based model provides travel demand to TRANSIMS network simulation model Minute-by-minute Parcel-to-parcel Detailed market segments (toll/notoll, trip-specific VOT) 1 hour to simulate 1 million people on laptop, ½ hour on server TRANSIMS provides information on network performance by time-of-day, as detailed as: 10 minute skims Activity locations ~50 VOT classes in assignment “Studio” controls model system execution and equilibration
4
Application Considerations
Different policy questions require different methods for running the model system Disaggregate framework Supports more detailed analysis Extracting, managing and interpreting results is straightfoward Volume of information is significant Simulation variation Not an issue for activity-model Significant issue in network simulation
Planning & Operations Planning Operations
5
Conclusions
Integrated model system is more sensitive to a wider range of policies produces a wider range of statistics of interest to decision makers Level of effort required to effectively test different types of improvements varied widely Debugging the model system, and individual scenarios was the greatest challenge Must have willingness to investigate and experiment
6
Additional C10 Insights
Examples of sensitivity tests Linkage vs integration Equilibration and convergence Consistency
7
Freeway Tolling: Demand Impacts
Trips shift out of peaks and midday and into evening and early AM Higher tolls increases the magnitude of this shift 4000 3000 2000 1000 0 -1000 -2000 -3000 -4000
Difference in Trips by Time of Day
PRICING_3 PRICING_4 PRICING_5 Time shifting varies by purpose Work trips shift into early AM and out of AM peak Social/recreation trips shift significantly out of peaks and primarily into the evening 10.0% 8.0% 6.0% 4.0% 2.0% 0.0%
Work & Soc/Rec Trips by Time of Day
BASE-WORK PRICING_5-WORK BASE-SOCREC PRICING_5-SOCREC
8
Travel Demand Management
“Flexible Schedule” scenario Asserted assumptions about: Fewer individual work activities Longer individual work durations Aggregate work durations constant Target: Fulltime Workers
Tours by Purpose (Fulltime Workers)
Work School Escort Pers Bus Shop Meal Soc/Rec Workbased Total
Original
94,408 115 8,070 13,519 10,531 3,817 13,076 27,949 171,485
Adjusted
78,472 140 9,023 16,848 12,938 3,842 14,360 23,211 158,834
Adj/Orig
0.83
1.22
1.12
1.25
1.23
1.01
1.10
0.83
0.93
Work Tour Duration Distribution
8 7 1 0 3 2 6 5 4 Original Adjusted
9
Linkage vs Integration
Establishing linkages, not true integration C10 goal of working with the existing tools and capabilities Integration may require more fundamental reformulations “Demand” vs “Supply Models Demand models as “planning models” – most build schedule a priori, and don’t reflect time-dependency throughout the day DTA as “dynamic models” Mathematical formulations and behavioral theory Lack of unifying behavioral theory Differences in formulation and foundations between demand and supply models.
Mathematical formulations should follow behavioral theory
10
Linkage Challenges
Equilibration & Uniqueness
Unclear how to address within the context of complex simulation tools Relevance to linked, advanced demand and supply models Relevance to reality?
Need to consider multiple metrics
Gap Consistency Stability
Practical issues of network supply runtime
11
Convergence Testing
Convergence Necessary to ensure usefulness of model system Given the same inputs, will the model system produce the same outputs?
Can significantly influence the conclusions drawn Network and system convergence Extensive testing of different strategies Network temporal resolution Successive iteration feedback Subselection
TEST_1.13_sqrt-1-over-N (G=3)
100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 100,000 2 6 10 14 18 22 26 30 34 38 42 46 50 54 58 62 66
Assignment Iteration (N) TEST_1.2_5min (G=3)
70 74 78 82 86 90 0.00
0.50
90,000 0.45
80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0.10
0.05
2 6 10 14 18 22 26 30 34 38 42 46 50 54
Assignment Iteration (N)
58 62 66 70 74 78 82 86 90 0.00
0.25
0.20
0.15
0.40
0.35
0.30
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
MSim problems Router problems Select Link Vol VHT (EQUI) / 10 TripGap RelGap MSim problems Router problems Select Link Vol VHT (EQUI) / 10 TripGap RelGap
12
Lessons Learned: Application
Level of convergence can significantly influence the conclusions drawn from alternative analyses.
13
Consistency
Convergence not meaningful if there are egregious inconsistencies Temporal Spatial Typological Example: demand model employs trip-segmented VOT, but then a single VOT used in network model Activity models (typically) (Relatively) coarse temporal resolution Typological detail Dynamic network models (typically) Temporal detail Coarse typological resolution
14
Temporal Consistency
Base
Total Schedule Difference by Time-of-Day (Daysim Only) FIXED DEPARTURES: NO FURTHER ADJUSTMENTS
Even if consistent in structure or resolution, there can still be issues with outcome consistency Ensure that the detailed schedules produced by the DaySim model are maintained in the TRANSIMS network model Inconsistencies are inevitable – how to resolve Maintain activity durations or departure times?
Allow supply model to reschedule 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0
Spatial Detail
Total Schedule Difference by Time-of-Day (Daysim Only) FIXED DEPARTURES: NO FURTHER ADJUSTMENTS
'3.10
'3.20
'3.30
'3.40
'3.50
'3.60
'3.70
'3.80
'3.90
'3.10
'3.20
'3.30
'3.40
'3.50
'3.60
'3.70
'3.80
'3.90
15
Transferability
Estimated difference between Tampa and Jacksonville coefficient estimates % of coefficients by type of choice model Trip time of day Intermediate stop location Intermediate stop generation Other HB tour mode Other HB tour time of day Other tour destination School tour mode WB subtour generation Work tour mode Work tour time of day Exact number of tours Person-day tour generation Auto ownership Usual work location 0% 20% 40% 60% 80% 100% significant difference insignificant difference not estimable
16
Transferability
Estimated difference between Tampa and Jacksonville coefficient estimates % of coefficients by type of variable logsum from lower model time schedule measure land use measure impedance measure tour/trip characteristic day-pattern characteristic household characteristic person characteristic alt-specific constant 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% significant difference insignificant difference not estimable
17
Future Efforts
Reconsideration of the fundamental “demand-supply” linkage How can models be more tightly integrated?
Can integrated solution methods be defined?
Does equilibrium exist in reality, and if not what are the implications?
How can advanced models be implemented and applied most effectively?
18